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How Russia's oldest bank found itself on the leading edge of in-memory computing

Katherine Noyes | June 1, 2016
'The systems built 15 years ago couldn't cope,' one executive says.

Many financial-services offerings today are also packages that cross product boundary lines, making integration even more important.

As smaller, nimbler banks increasingly emerged with more flexible IT, the pressure was on to implement something different. Inspiration came from technologies that had supported the rapid growth of Internet giants like Facebook and Google.

"They grew from startups and never had the money for big mainframes, so usually they bought small machines," Khasin explained. "When they grew, they bought more."

With similar scalability in mind, Sberbank zeroed in on in-memory data grid technology, through which data structures reside in RAM and are distributed among multiple commodity servers. Often aimed at big-data applications, in-memory computing promises new levels of performance and scale on standard, inexpensive hardware.

'It will be a client-centric architecture'

Numerous vendors offer such technology, including big names such as SAP and Oracle, and Sberbank spent the better part of 2015 conducting pilot tests. At the end of that year, it settled on GridGain Systems' In-Memory Data Fabric platform. The first pieces of its new technology recently came into production.

GridGain offers clustering and compute capabilities, database-agnostic data processing and a real-time streaming engine as well as Hadoop acceleration. It can connect multiple data sources -- including relational and NoSQL databases -- with Java, .NET and C++ applications in a distributed, massively parallel architecture for high-speed access and processing.

So far, Sberbank has implemented a grid of three "nodes," or machines, for processing payments using GridGain's in-memory technology and industry-standard hardware. Performance and time to market are among the top benefits Khasin cites. Horizontal scalabililty is limited only by the number of nodes in place, and performance has increased by at least a factor of 10, Khasin said. Hardware costs, meanwhile, are minimal.

Training has also been less of an issue than it might have been for Sberbank because GridGain's software is based on open-source Apache Ignite technology, and has been openly worked on by a large community, Khasin said. Hundreds of the bank's 5,000 or so software developers have been trained in using GridGain, and now they're contributing back to the community as well.

Among the biggest challenges Sberbank has faced so far is the newness of in-memory technology and the relative lack of expertise on the market in implementing it. The size of the bank's implementation -- ultimately encompassing several petabytes of data -- makes that challenge even tougher.

As the implementation scales, there's still more work to be done on a fault tolerance layer, Khasin said. But he has no doubt the technology is the wave of the future.

"Banks are usually conservative, but everybody believes this is the right direction," he said. "Otherwise, you spend all your IT budget on hardware upgrades."

 

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